Department of Computer Science, Faculty of Science, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3, Canada.
Sensors (Basel). 2022 Mar 23;22(7):2467. doi: 10.3390/s22072467.
Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients' heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.
心血管疾病是全球范围内的主要死因，每年导致近 1790 万人死亡。因此，早期发现和治疗对于改善这种情况至关重要。许多制造商已经开发出产品来监测患者在进行日常活动时的心脏状况。然而，很少有产品能够诊断复杂的心脏异常，而不仅仅是检测节律波动。本文提出了一种新方法，将心电图信号的短时傅里叶变换（STFT）频谱图与手工特征相结合，以检测商业产品能力之外的心脏异常。使用所提出的卷积神经网络，该算法可以以 99.79%的准确率检测 16 种不同的节律异常，假阳性率为 0.15%，敏感性为 99.74%。此外，相同的算法还可以以 99.18%的准确率检测 13 种心跳异常，假阳性率为 0.45%，敏感性为 98.80%。
